Search: coding
Search MCP servers and agent skills by name, description, category or topic — 442 results.
Coding-Solo/godot-mcp
A MCP server for interacting with the Godot game engine, providing tools for editing, running, debugging, and managing scenes in Godot projects.
Jovancoding/Network-AI
Multi-agent orchestration MCP server with race-condition-safe shared blackboard. 20+ MCP tools: blackboard read/write, agent spawn/stop, FSM transitions, budget tracking, token management, and audit log query. `npx network-ai-server --port 3001`.
honeydew-ai/honeydew-ai-coding-agents-plugins
11 skills for the Honeydew semantic layer over Snowflake, Databricks, and BigQuery: model exploration, entity/relation/attribute/metric/context/domain creation, validation, query, filtering, and workspace branching
Leonxlnx/taste-skill
High-agency frontend skill that gives AI good taste with tunable design variance, motion intensity, and visual density to stop generic UI slop
oraios/serena
A fully-featured coding agent that relies on symbolic code operations by using language servers.
GLips/Figma-Context-MCP
Provide coding agents direct access to Figma data to help them one-shot design implementation.
wonderwhy-er/DesktopCommanderMCP
A swiss-army-knife that can manage/execute programs and read/write/search/edit code and text files.
safedep/vet
vet-mcp checks open source packages—like those suggested by AI coding tools—for vulnerabilities and malicious code. It supports npm and PyPI, and runs locally via Docker or as a standalone binary for fast, automated vetting.
repowise-dev/repowise
Codebase intelligence for AI coding agents. Indexes a repo into a dependency graph, git history, defect-risk code health, auto-generated docs, and architectural decisions, served as 9 task-shaped MCP tools across 15 languages. Runs 100% local; hosted option available.
NVIDIA/CUDA-Q/cudaq-guide
CUDA-Q onboarding guide for installation, test programs, GPU simulation, QPU hardware, and quantum applications.
NVIDIA/DALI/dali-dynamic-mode
Use when writing DALI data loading or preprocessing code with `nvidia.dali.experimental.dynamic` (ndd), or when converting DALI pipeline-mode code to dynamic mode, or when the user...
NVIDIA/Megatron-Bridge/adding-model-support
Guide for adding support for new LLM or VLM models in Megatron-Bridge.
NVIDIA/Megatron-Bridge/build-and-dependency
Dev environment setup for Megatron Bridge — container-based development, uv package management, lockfile regeneration, adding dependencies, Slurm container usage, and common build...
NVIDIA/Megatron-Bridge/bump-dependency
Bump a pinned dependency (TransformerEngine, Megatron-LM, NRX, etc.), regenerate the lockfile, open a PR, and drive it to green by attaching a watchdog to the "CICD NeMo" workflow...
NVIDIA/Megatron-Bridge/cicd
CI/CD reference for Megatron Bridge — pipeline structure, commit and PR workflow, CI failure investigation, and common failure patterns.
NVIDIA/Megatron-Bridge/linting-and-formatting
Code style and quality rules for Megatron Bridge — ruff configuration, naming conventions, type hints, mypy rules, docstrings, copyright headers, logging, and the code review check...
NVIDIA/Megatron-Bridge/mlm-bridge-training
Run Megatron-LM (MLM) and Megatron Bridge training with mock or real data.
NVIDIA/Megatron-Bridge/multi-node-slurm
Convert single-node scripts to multi-node Slurm sbatch jobs and debug common multi-node failures.
NVIDIA/Megatron-Bridge/nemo-rl-e2e-testing
External NeMo-RL end-to-end validation workflow for Megatron-Bridge model/provider changes, including downstream compatibility checks, external RL lifecycle behavior, Megatron poli...
NVIDIA/Megatron-Bridge/parity-testing
Structured framework for verifying numerical parity of HFMCore weight conversions.
NVIDIA/Megatron-Bridge/perf-activation-recompute
Validate and use selective and full activation recompute in Megatron Bridge to reduce GPU memory usage at the cost of extra compute.
NVIDIA/Megatron-Bridge/perf-cpu-offloading
Validate and use CPU offloading in Megatron Bridge, including layer-level activation offloading and fractional optimizer state offloading with HybridDeviceOptimizer.
NVIDIA/Megatron-Bridge/perf-cuda-graphs
Validate and use CUDA graph capture in Megatron Bridge, including local full-iteration graphs and Transformer Engine scoped graphs for attention, MLP, and MoE modules.
NVIDIA/Megatron-Bridge/perf-expert-parallel-overlap
Validate and use MoE expert-parallel communication overlap in Megatron-Bridge, including overlap_moe_expert_parallel_comm, delay_wgrad_compute, and flex dispatcher backends such as...
NVIDIA/Megatron-Bridge/perf-hierarchical-context-parallel
Operational guide for enabling hierarchical context parallelism in Megatron-Bridge, including config knobs, code anchors, pitfalls, and verification.
NVIDIA/Megatron-Bridge/perf-megatron-fsdp
Operational guide for enabling Megatron FSDP in Megatron-Bridge, including config knobs, code anchors, pitfalls, and verification.
NVIDIA/Megatron-Bridge/perf-memory-tuning
Techniques for reducing peak GPU memory in Megatron Bridge — expandable segments, parallelism resizing, activation recompute, CPU offloading constraints, and common OOM fixes.
NVIDIA/Megatron-Bridge/perf-moe-comm-overlap
MoE expert-parallel communication overlap in Megatron Bridge.
NVIDIA/Megatron-Bridge/perf-moe-dispatcher-selection
Choose the right MoE token dispatcher (`alltoall`, DeepEP, or HybridEP) for the hardware, EP degree, and optimization stage.
NVIDIA/Megatron-Bridge/perf-moe-hardware-configs
Representative MoE training playbooks by hardware platform and model family.
NVIDIA/Megatron-Bridge/perf-moe-long-context
Long-context MoE training guidance for Megatron Bridge.
NVIDIA/Megatron-Bridge/perf-moe-optimization-workflow
Systematic workflow for MoE training optimization in Megatron Bridge, based on the Megatron-Core MoE paper.
NVIDIA/Megatron-Bridge/perf-moe-vlm-training
Practical guidance for training MoE VLMs in Megatron Bridge.
NVIDIA/Megatron-Bridge/perf-parallelism-strategies
Operational guide for choosing and combining parallelism strategies in Megatron Bridge, including sizing rules, hardware topology mapping, and combined parallelism configuration.
NVIDIA/Megatron-Bridge/perf-sequence-packing
Validate and use packed sequences and long-context training in Megatron-Bridge, distinguishing offline packed SFT for LLMs from in-batch packing for VLMs, and applying the right CP...
NVIDIA/Megatron-Bridge/perf-tp-dp-comm-overlap
Operational guide for enabling TP, DP, and PP communication overlap in Megatron-Bridge, including config knobs, code anchors, pitfalls, and verification.
NVIDIA/Megatron-Bridge/recipe-recommender
Recommend and customize Megatron Bridge recipes for a user's model, GPU count, and training goal.
NVIDIA/Megatron-Bridge/resiliency
Resiliency features in Megatron Bridge including fault tolerance, straggler detection, in-process restart, preemption, and re-run state machine.
NVIDIA/Megatron-Bridge/testing
Testing reference for Megatron Bridge — unit and functional test layout, tier semantics (L0/L1/L2/flaky), script conventions, running tests locally, adding/moving/disabling tests,...
NVIDIA/Megatron-Bridge/verl-e2e-testing
External verl end-to-end validation workflow for Megatron-Bridge model/provider changes.
NVIDIA/Megatron-Core/build-and-dependency
Container-based dev environment setup and dependency management for Megatron-LM.
NVIDIA/Megatron-Core/bump-base-image
Bump the NVIDIA PyTorch base image (`nvcr.io/nvidia/pytorch:-py3`) used by Megatron-LM CI.